气候变化领域集成服务门户(CCPortal): Multi-timescale drought prediction using new hybrid artificial neural network models

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DOI	10.1007/s11069-021-04550-x
	Multi-timescale drought prediction using new hybrid artificial neural network models
	Banadkooki F.B.; Singh V.P.; Ehteram M.
发表日期	2021
ISSN	0921030X
起始页码	2461
结束页码	2478
卷号	106 期号:3
英文摘要	In this study, new hybrid artificial neural network (ANN) models were used for predicting the groundwater resource index. The salp swarm algorithm (SSA), particle swarm optimization (PSO), and genetic algorithm (GA) were used to find the weight and bias values of the ANN models. The ANN-PSO, ANN-SSA and ANN-GA models were used to predict the groundwater resource index (GRI)-based drought at different timescales (6, 12, and 24 months) in Yazd plain, Iran. Five input scenarios were used for modeling GRI. The best input scenario was a combination of one-month-lagged GRI, two-month-lagged GRI, three-month-lagged GRI, four-month-lagged GRI, and five-month-lagged GRI, which is known as the fifth input scenario. The outputs of models indicated that the ANN-SSA model with input scenario (5) decreased the mean absolute error (MAE) of ANN-PSO (5) and ANN-GA (5) by 43% and 51%, respectively. Among the hybrid ANN models, ANN-SSA (5), ANN-PSO (5) and ANN-GA (5) outperformed the other hybrid ANN models. © 2021, The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature.
关键词	Drought estimation Groundwater index Salp swarm algorithm Soft computing models
英文关键词	algorithm; artificial neural network; computer simulation; drought; error analysis; estimation method; groundwater resource; numerical model; prediction; resource assessment; resource management
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/206099
作者单位	Agricultural Department, Payame Noor University, Tehran, Iran; Department of Biological and Agricultural Engineering, Zachry Department of Civil Engineering, Texas A & M University, College Station, TX 77843-2117, United States; Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran
推荐引用方式 GB/T 7714	Banadkooki F.B.,Singh V.P.,Ehteram M.. Multi-timescale drought prediction using new hybrid artificial neural network models[J],2021,106(3).
APA	Banadkooki F.B.,Singh V.P.,&Ehteram M..(2021).Multi-timescale drought prediction using new hybrid artificial neural network models.Natural Hazards,106(3).
MLA	Banadkooki F.B.,et al."Multi-timescale drought prediction using new hybrid artificial neural network models".Natural Hazards 106.3(2021).